Book Image

Python Web Scraping Cookbook

By : Michael Heydt
Book Image

Python Web Scraping Cookbook

By: Michael Heydt

Overview of this book

Python Web Scraping Cookbook is a solution-focused book that will teach you techniques to develop high-performance scrapers and deal with crawlers, sitemaps, forms automation, Ajax-based sites, caches, and more. You'll explore a number of real-world scenarios where every part of the development/product life cycle will be fully covered. You will not only develop the skills needed to design and develop reliable performance data flows, but also deploy your codebase to AWS. If you are involved in software engineering, product development, or data mining (or are interested in building data-driven products), you will find this book useful as each recipe has a clear purpose and objective. Right from extracting data from the websites to writing a sophisticated web crawler, the book's independent recipes will be a godsend. This book covers Python libraries, requests, and BeautifulSoup. You will learn about crawling, web spidering, working with Ajax websites, paginated items, and more. You will also learn to tackle problems such as 403 errors, working with proxy, scraping images, and LXML. By the end of this book, you will be able to scrape websites more efficiently and able to deploy and operate your scraper in the cloud.
Table of Contents (13 chapters)

Introduction

In this chapter, we will learn to containerize our scraper, getting it ready for the real world by starting to package it for real, modern, cloud-enabled operations. This will involve packaging the different elements of the scraper (API, scraper, backend storage) as Docker containers that can be run locally or in the cloud. We will also examine implementing the scraper as a microservice that can be independently scaled.

Much of the focus will be upon using Docker to create our containerized scraper. Docker provides us a convenient and easy means of packaging the various components of the scraper as a service (the API, the scraper itself, and other backends such as Elasticsearch and RabbitMQ). By containerizing these components using Docker, we can easily run the containers locally, orchestrate the different containers making up the services, and also conveniently...